Method and apparatus for siloxane measurements in a biogas

ABSTRACT

A method for monitoring of siloxane compounds in a biogas includes the step of generating a first absorption spectrum based on a ratio of a first spectral measurement and a second spectral measurement. The first spectral measurement is from a non-absorptive gas having substantially no infrared absorptions in a specified wavelength range of interest and the second spectral measurement is from a sample gas comprising the biogas. The method also includes the step of calculating a concentration of at least one siloxane compound in the biogas using a second absorption spectrum based on, at least, a first individual absorption spectrum for a known concentration of the at least one siloxane compound.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation-in-part of prior co-pendingU.S. application Ser. No. 12/567,981 filed on Sep. 28, 2009, which is acontinuation of prior U.S. application Ser. No. 12/119,244 filed on May12, 2008, which is a continuation of prior U.S. application Ser. No.11/240,799, filed on Sep. 30, 2005, the entire disclosures of which areincorporated by reference herein.

FIELD OF THE INVENTION

The invention generally relates to absorption spectrometers, and moreparticularly to detecting trace amounts of chemical warfare agents,toxic industrial chemicals, and other trace compounds that can be foundin ambient air. The invention also relates to monitoring and measuringconcentrations of siloxane compounds in, for example, a biofuel orbiogas.

BACKGROUND OF THE INVENTION

Spectroscopy is the study of the interaction between electromagneticradiation and a sample (e.g., containing one or more of a gas, solid andliquid). The manner in which the radiation interacts with a particularsample depends upon the properties (e.g., molecular composition) of thesample. Generally, as the radiation passes through the sample, specificwavelengths of the radiation are absorbed by molecules within thesample. The specific wavelengths of radiation that are absorbed areunique to each of the molecules within the specific sample. Byidentifying which wavelengths of radiation are absorbed, it is thereforepossible to identify the specific molecules present in the sample.

Infrared spectroscopy is a particular field of spectroscopy in which,for example, the types of molecules and the concentration of individualmolecules within a sample are determined by subjecting the sample (e.g.,gas, solid, liquid or combination thereof) to infrared electromagneticenergy. Generally, infrared energy is characterized as electromagneticenergy having wavelengths of energy between about 0.7 μm (frequency14,000 cm⁻¹) and about 1000 μm (frequency 10 cm⁻¹). Infrared energy isdirected through the sample and the energy interacts with the moleculeswithin the sample. The energy that passes through the sample is detectedby a detector (e.g., an electromagnetic detector). The detected signalis then used to determine, for example, the molecular composition of thesample and the concentration of specific molecules within the sample.

One particular type of infrared spectrometer is the Fourier TransformInfrared (FTIR) spectrometer. They are used in a variety of industries,for example, air quality monitoring, explosive and biological agentdetection, semiconductor processing, and chemical production. Differentapplications for FTIR spectrometers require different detectionsensitivity to enable a user to distinguish between which molecules arepresent in a sample and to determine the concentration of the differentmolecules. In some applications, it is necessary to identify theconcentration of individual molecules in a sample to within about onepart per billion (ppb). As industrial applications require increasinglybetter sensitivity, optimization of existing spectroscopy systems andutilization of new spectroscopy components can enable the system torepeatably and reliably resolve smaller and smaller concentrations ofmolecules in samples.

FTIR spectrometers can also be used to monitor concentrations ofcompounds, e.g., in gases. Biofuels (e.g., biogas) are used to powervarious equipment, including turbine generators. The biogas is burned topower the equipment. Biogas (e.g., gas from animal waste, wastewater ora landfill) can include, a variety of compounds, including, siloxanecompounds. Siloxane compounds in the biogas are also burned whichcreates oxides (e.g., SiO₂ (e.g., silica, or sand)). The SiO₂ can coatboth the turbine blades as well as the turbine bearings, resulting indecreased performance or even failure of the turbine. The coatingprocess is accelerated with higher levels of siloxane in the biogas.Biogas producers usually use an activated charcoal filter to trap thesiloxanes, however, when the filter is expended the siloxane levelrises.

Traditional methods for monitoring concentrations of siloxane compoundsin a biogas are performed offline by analyzing samples taken from thebiogas. For example, traditional techniques involve using GC/MS (i.e.,gas chromatography/mass spectrometry) techniques to separate thesiloxanes from the background gas and measure them. To analyze thesample gas, a sample is grabbed for analysis and run on the GC/MSsystem. A field sample is usually taken from the gas stream andintroduced into either a stainless steel canister, a Tedlar sample bagor collected using a Methanol solvent impinger. This sample is thentransported back to the analytical lab and analyzed; the analyticalresult is usually not known for days. Samples have the tendency to letcomponents condense out which makes it hard to assess the truecomposition in the sample. Samples taken in this manner also onlyprovide a single shot in time at which the contents are analyzed andtherefore, may not be representative of the true composition of thesample. The GC/MS analysis of the sample can also take several hours toanalyze the siloxane compounds in the sample which may be too late toallow for operator intervention. If a rise in siloxane levels hadoccurred, the opportunity to perform any actionable recourse may havealready passed. Enhancing the ability to monitor and measureconcentrations of siloxane in a biogas can enable greater turbine life.Furthermore, being able to monitor and quickly detect and quantifysiloxane compounds can provide greater time for actionablerecourse/intervention.

SUMMARY OF THE INVENTION

Spectroscopy can be used to detect, identify, and/or quantify traceamounts of siloxane compounds in, for example, a biogas (e.g., identifythe concentration of individual siloxane compounds in a sample biogas towithin about five parts per billion (ppb)). Trace amounts of both cyclicsiloxanes (e.g., D3-siloxane, D4-siloxane, D5-siloxane and D6-siloxane)and linear siloxanes (L2-siloxane, L3-siloxane, L4-siloxane andL5-siloxane) in a biogas can be detected and quantified. Concentrationsof siloxane can be measured in-situ (e.g., at the site of, for example,a landfill, animal waste site or wastewater site) and in real time(e.g., processing and analyzing the content of the sample biogas at asite without having to obtain a sample and analyze the sample at alaboratory at a relatively later point in time). An in-line continuousmonitor can sense a rise in siloxane levels in real time and notify theoperator or automatically shut down the process, preventingunnecessarily exposing the turbines to SiO₂.

A sample that includes agents (e.g., compounds) having substantiallyhigher infrared absorptions (e.g., interfering absorbers), as comparedto other agents in the sample can present problems in FTIR spectroscopybecause FTIR relies on subjecting a sample to infrared energy.Interfering absorbers in the sample prevent effective detection andmeasurement of concentrations of the other agents to be detected in thesample which have substantially lower infrared absorptions. A biogas caninclude molecules such as, for example, siloxane compounds, hydrocarboncompounds (e.g., methane or ethane), water, or carbon dioxide. Thehydrocarbon compounds in the biogas can have relatively high infraredabsorptions at certain wavelengths (e.g., absorption of about 0.055 at awavelength of about 7.8 microns for ethane) as compared to siloxanecompounds (e.g., an absorption of about 0.001 at a wavelength of about7.8 microns for a D4 siloxane). The hydrocarbons can therefore beinterfering absorbers. Siloxane compounds can have relatively higherinfrared absorptions in a wavelength range of about 8 microns to about12 microns (e.g., an absorption of about 0.075 at about 8.2 microns and0.125 at about 11 microns for D4 siloxane). Therefore, concentrations ofsiloxane compounds in a sample biogas can be measured by taking spectralmeasurements in a wavelength range of interest (e.g., about 8 microns toabout 12 microns), even in the presence of hydrocarbon compounds orother interfering absorbers. The wavelength range of interest can beselected where the major components of the biogas (e.g., H₂O, CO₂, CH₄)do not have large absorbances. The siloxane compounds may haveoverlapping absorbances with other hydrocarbons in the wavelength rangeof interest. Multivariate analysis methods can be used to distinguishthe contributions between the siloxane compounds and the otherhydrocarbons, as well as to assess the contributions that are duestrictly to the siloxane compounds.

In one aspect, the invention features a method for monitoring ofsiloxane compounds in a biogas. The method includes the step ofgenerating a first absorption spectrum based on a ratio of a firstspectral measurement from a non-absorptive gas having substantially noinfrared absorptions in a specified wavelength range of interest and asecond spectral measurement from a sample gas comprising the biogas. Themethod also includes calculating a concentration of at least onesiloxane compound in the biogas using a second absorption spectrum. Thesecond absorption spectrum is based on, at least, a first individualabsorption spectrum for a known concentration of the at least onesiloxane compound.

In one embodiment, a processor is used to calculate a concentration ofat least one siloxane compound in the biogas using a chemometricalgorithm. For example, a processor can be used to perform multipleregression analysis using the first absorption spectrum and the secondabsorption spectrum to calculate the concentration of at least onesiloxane compound in the biogas. Multiple regression analysis can beperformed using Classical Least Squares (CLS), Partial Least Squares(PLS), Inverse Least Squares (ILS) or Principal Component Analysis(PCA).

The second absorption spectrum can be created based on, at least, thefirst individual absorption spectrum and individual absorption spectrafor one or more additional siloxane compounds, hydrocarbon compounds,water or carbon dioxide. The second absorption spectrum can be a modelbased on known concentrations of the siloxane compounds, hydrocarboncompounds, water or carbon dioxide. In some embodiments, the secondabsorption spectrum is a model based on, at least, the first individualabsorption spectrum. A concentration of at least one siloxane compoundcan be calculated by providing at least one variable representing theconcentration of the at least one siloxane compound and determining avalue for the at least one variable such that that the second absorptionspectrum is substantially similar to the first absorption spectrum(e.g., mathematically fitting the second absorption spectrum to thefirst absorption spectrum).

In some embodiments, the concentration of at least one siloxane compoundis calculated using a processor, in real-time (e.g., results ofquantifying concentrations of siloxane compounds obtained in seconds orminutes) and in-situ (e.g., in-line or in a device in fluidcommunication with a source of the biogas and without the need forgrabbing a sample). The second spectral measurement can be taken over anacquisition period of about 10 seconds to about 20 seconds.

In some embodiments, the at least one siloxane compound is selected froma group consisting of L2-siloxane, L3-siloxane, L4-siloxane,L5-siloxane, D3-siloxane, D4-siloxane, D5-siloxane, or D6-Siloxane.

The non-absorptive gas and the biogas can be provided to a sample cell.In some embodiments, the sample cell includes a concave reflective fieldsurface at a first end of the sample cell and a substantially spherical,concave reflective objective surface at a second end of the sample cellin a confronting relationship to the field surface, the objectivesurface having a cylindrical component increasing coincidence of foci inat least one plane to maximize throughput of the second beam ofradiation propagating through the sample cell via multiple reflectionson each of the field surface and the objective surface.

In another aspect, the invention features a method for monitoring alevel of at least one siloxane compound in a biogas. The method includesthe step of providing a non-absorptive gas to a sample cell, thenon-absorptive gas having substantially no infrared absorptions in aspecified wavelength range of interest. The method also includes thesteps of taking a first spectral measurement from the sample cell andproviding a biogas to the sample cell, the biogas comprising at leastone siloxane compound. The method also includes taking a second spectralmeasurement from the sample cell, generating a first absorption spectrumbased on a ratio of the first spectral measurement to the secondspectral measurement and calculating a concentration of the at least onesiloxane compound in the biogas by using the first absorption spectrumand a second absorption spectrum. The second absorption spectrum isbased on, at least, an individual absorption spectrum for a knownconcentration of the at least one siloxane compound.

In some embodiments, the method includes performing, using a processor,a multiple regression analysis to calculate the concentration of atleast one siloxane compound. Multiple regression analysis can beperformed using Classical Least Squares (CLS), Partial Least Squares(PLS), Inverse Least Squares (ILS), or Principal Component Analysis(PCA).

The second absorption spectrum can be a model based on, at least, theindividual absorption spectrum for the at least one siloxane compoundand individual absorption spectra for one or more additional siloxanecompounds, hydrocarbon compounds, water or carbon dioxide. In someembodiments, the second absorption spectrum is a model (e.g., a modelrepresentative of the individual absorption spectra of the agents in thebiogas) based on known concentrations of the siloxane compounds,hydrocarbon compounds, water or carbon dioxide.

In some embodiments, a value for the concentration for the at least onesiloxane compound is determined such that the second absorption spectrum(e.g., the model spectrum) is substantially similar to the firstabsorption spectrum (e.g., mathematically fitting the second absorptionspectrum to the first absorption spectrum).

In some embodiments, the concentration of the at least one siloxanecompound is calculated real-time (e.g., results of quantifyingconcentrations of siloxane compounds obtained in seconds or minutes) andin-situ (e.g., in-line or in a device in fluid communication with asource of the biogas and without the need for grabbing a sample).

The biogas can be provided from animal waste, wastewater or a landfill.In some embodiments, a turbine generator is shut off when theconcentration of at least one siloxane compound reaches a thresholdvalue.

In some embodiments, the second spectral measurement from the samplecell is taken in a wavelength range of about 8 microns to about 12microns. The second spectral measurement can be taken over a 10 secondacquisition time period. The second spectral measurement can be takenover different acquisition time periods (e.g., 1 second or greater or100 seconds or less). In some examples, a typical range for acquisitiontime periods is between 10 seconds to 2 minutes. The step of taking thesecond spectral measurement comprises acquiring an infrared signal fromthe sample cell. The second absorption spectrum can be based on atleast, an individual absorption spectra for L2-siloxane, L3-siloxane,L4-siloxane, L5-Siloxane, D3-siloxane, D4-siloxane, D5-Siloxane,D6-siloxane, Methane, Ethane, water, carbon dioxide, or an combinationthereof.

In yet another aspect, the invention features a system for monitoring atleast one siloxane compound in a biogas. The system includes a source ofa first beam of radiation, an interferometer receiving the first beam ofradiation from the source and forming a second beam of radiationcomprising an interference signal and a sample cell in opticalcommunication with the interferometer. The system also includes a flowmechanism establishing a first flow of a non-absorptive gas havingsubstantially no infrared absorptions in a specified wavelength range ofinterest and a second flow of a biogas through the sample cell. A cooleddetector is in optical communication with the sample cell, the cooleddetector receiving a first interference signal propagating through thenon-absorptive gas in the sample cell and a second interference signalpropagating through a sample gas in the sample cell, the sample gascomprising the biogas. The system also includes a processor inelectrical communication with the cooled detector, the processorconfigured to calculate a concentration of at least one siloxanecompound in the biogas based on a first absorption spectrum based onratio of the first interference signal to the second interference signaland a second absorption spectrum based on, at least, an individualabsorption spectrum for a known concentration of the at least onesiloxane compound. The system also includes a housing in which thesource, the interferometer, the sample cell, the cooled detector and theprocessor are disposed.

In some embodiments, the sample cell includes a concave reflective fieldsurface at a first end of the sample cell and a substantially spherical,concave reflective objective surface at a second end of the sample cellin a confronting relationship to the field surface, the objectivesurface having a cylindrical component increasing coincidence of foci inat least one plane to maximize throughput of the second beam ofradiation propagating through the sample cell via multiple reflectionson each of the field surface and the objective surface.

In some embodiments, the second absorption spectrum is a model based on,at least, the individual absorption spectrum for the at least onesiloxane compound and individual absorption spectra for one or moreadditional siloxane compounds, hydrocarbon compounds, water or carbondioxide. The second absorption spectrum can be a model based on, atleast, known concentrations of the siloxane compounds, hydrocarboncompounds, water or carbon dioxide.

In another aspect, the invention features a computer readable product,tangibly embodied on an information carrier or a machine-readablestorage device, and operable on a digital signal processor for a biogasdetection system. The computer readable product includes instructionsoperable to cause the digital signal processor to receive a firstspectral measurement from a non-absorptive gas in a sampling cell, thenon-absorptive gas having substantially no infrared absorptions in aspecified wavelength range of interest. The product also includesinstructions operable to cause the digital signal processor to receive asecond spectral measurement from a sample gas comprising a biogas in thesampling cell, generate a first absorption spectrum based on a ratio ofthe first spectral measurement and the second spectral measurement,generate a second absorption spectrum based on, at least, a firstindividual absorption spectrum for a known concentration of at least onesiloxane compound and perform a multiple regression analysis using thefirst absorption spectrum and the second absorption spectrum tocalculate a concentration of the at least one siloxane compound in thebiogas.

In some embodiments, the digital processor is configured to determine avalue for the concentration of the at least one siloxane compound in thebiogas such that the second absorption spectrum is substantially similarto the first absorption spectrum. The digital processor can beconfigured to generate the second absorption spectrum which is a modelbased on, at least, the first individual absorption spectrum and anindividual absorption spectra for one or more additional siloxanecompounds, hydrocarbon compounds, water or carbon dioxide.

The invention, in various embodiments, features a spectroscopicdetection system for monitoring and/or detecting toxic chemicalsubstances in a gas sample, such as ambient air. The system can be acompact, portable multiple gas analyzer capable of detecting anddiscriminating a broad range of chemical constituents including variouschemical warfare agents (CWAs), toxic organic compounds (TOCs), andtoxic industrial chemicals (TICs) at low or sub part per billion (ppb)levels. The system minimizes false alarms (e.g., false positives orfalse negatives), features high specificity, and can operate withresponse times on the order of a few seconds to a few minutes, dependingon the application.

In one embodiment, the system can be packaged as an unobtrusive,automated unit capable of being deployed in an air handling system of abuilding to provide a rapid sensitive threat alert sufficient to protectbuilding occupants and also allow adaptive infrastructure systems toreact to the presence of a contaminant. In one embodiment, the unit isan entirely self-contained analyzer, with a FTIR spectrometer, a gassample cell, a detector, an embedded processor, a display, an air pump,power supplies, heating elements, and other components onboard the unitwith an air intake to collect a sample and an electronic communicationsport to interface with other devices.

In one aspect, the invention features an apparatus capable of measuringa trace gas. The apparatus includes a source of a first beam ofradiation and an interferometer receiving the first beam of radiationfrom the source and forming a second beam of radiation including aninterference signal. A sample cell is in optical communication with theinterferometer, and the sample cell includes a concave reflective fieldsurface at a first end of the sample cell and a substantially spherical,concave reflective objective surface at a second end of the sample cell.The objective surface and the field surface are in a confrontingrelationship, and the objective surface includes a cylindrical componentincreasing coincidence of foci in at least one plane to maximizethroughput of the second beam of radiation propagating through thesample cell via multiple reflections on each of the field surface andthe objective surface. The apparatus also includes a flow mechanismestablishing a flow of a sample of gas through the sample cell, a cooleddetector in optical communication with the sample cell, and a processorin electrical communication with the cooled detector. The cooleddetector receives the interference signal propagating through the samplein the sample cell, and the processor determines from the interferencesignal an absorption profile for a trace gas in the sample. The source,interferometer, sample cell, cooled detector and processor can bedisposed in a housing.

In another aspect, the invention features a method of opticallymeasuring a trace gas. The method includes providing a portableabsorption spectrometer including a sample cell having a field surfaceat a first end and an objective surface at a second end in a confrontingrelationship to form a folded path, and flowing a sample of ambient airthrough the sample cell. The volume of the sample cell and the number ofpasses of a beam of radiation in the folded path can be optimized tomaximize throughput of the beam of radiation propagating in the samplecell to detect a trace gas having a concentration of less than about 500ppb in the sample of ambient air.

In still another aspect, the invention features a method of opticallymeasuring a trace gas. The method includes providing an absorptionspectrometer including a sample cell that is substantially air tight.The sample cell includes a field surface at a first end and an objectivesurface at a second end in a confronting relationship to direct a beamof radiation through the sample cell. A first signal of the beam ofradiation propagating through a sample of ambient air is measured at afirst pressure in the sample cell. The sample cell is pressurized withambient air to a second pressure, and a second signal of the beam ofradiation propagating through the sample of ambient air at the secondpressure is measured. The first signal and the second signal arecombined to determine a signal indicative of the presence of a tracegas.

In various embodiments, the first signal and the second signal can becombined to determine an absorption profile for the trace gas. In someembodiments, the beam of radiation can include an interference signal.In one embodiment, an absorption profile for the trace gas can bedetermined from the interference signal propagating through the samplein the sample cell. In one embodiment, by pressurizing the sample cell,the amplitude of the absorption profile of the trace gas can beincreased relative to a baseline signal.

In yet another aspect, the invention features a method of removing acontaminant from an optical system. The method includes a determining aconcentration of a contaminant in at least a sample region of anabsorption spectrometer and heating the sample region to adecontamination temperature to remove the contaminant if theconcentration of the contaminant exceeds a contamination value Theconcentration of the contaminant is monitored while heating the sampleregion, and the heating of the sample region can be abated or ceasedwhen the concentration of the contaminant reaches a decontaminationvalue.

In still another aspect, the invention features an apparatus capable ofmeasuring a trace gas. The apparatus includes an interferometerreceiving the first beam of radiation from a source and forming a secondbeam of radiation comprising an interference signal, a sample cell inoptical communication with the interferometer, a flow mechanismestablishing a flow of a sample of gas through the sample cell, a moduleto heat at least the sample cell, a detector in optical communicationwith the sample cell, and a processor in electrical communication withthe detector and the module. The detector receives the interferencesignal propagating through the sample in the sample cell. The processordetermines from the interference signal a concentration of a contaminantin the sample, signals the module to heat the sample cell to adecontamination temperature to remove the contaminant if theconcentration of the contaminant in the sample cell exceeds acontamination value, monitors the concentration of the contaminant whilethe module heats the sample cell, and signals the module to abate orcease heating the sample cell if the concentration of the contaminantreaches a decontamination value.

In other examples, any of the aspects above, or any apparatus or methoddescribed herein, can include one or more of the following features. Invarious embodiments, the trace gas has a concentration of less thanabout 500 ppb. In one embodiment, the trace gas has a concentration ofbetween about 10 ppb and about 50 ppb. In one embodiment, the housing isportable and defines a hole for intake of ambient air comprising thesample of gas. The hole can be in fluid communication with the samplecell. In one embodiment, the sample cell can have a volume of less thanabout 0.8 liters. In some embodiments, the sample cell can have apathlength of between about 5 meters and about 12 meters. In oneembodiment, the apparatus also includes a heating element disposed inthe portable housing to heat at least the sample cell to a temperatureof between about 40° C. and about 180° C.

In some embodiments, the housing is mountable in an air handling systemfor a building. An alarm can sound to alert to the presence of acontaminant in the air handling system.

In one embodiment, the sample of gas flows through the sample cell at arate of greater than about 3 liters per minute. The sample cell can havea gas exchange rate of between about 80% and about 95% in an interval oftime of about 10 seconds. In one embodiment, the sample of gas includesa chemical warfare agent, a toxic inorganic compound, or a toxic organiccompound. The apparatus can have a response time of less than about 20seconds for about 50 ppb of a gas such as sarin, tabun, soman, sulfurmustard, or VX.

In various embodiments, a first absorption spectrum can be measured at afirst resolution to detect the trace gas, and a second absorptionspectrum can be measured at a higher resolution. In some embodiments, afirst absorption spectrum can be measured at a first sensitivity todetect the trace gas, and a second absorption spectrum can be measuredat a higher sensitivity.

In various embodiments, the field surface can include a concavereflective surface and the objective surface can include a substantiallyspherical, concave reflective surface. The objective surface can includea cylindrical component increasing coincidence of foci in at least oneplane to maximize throughput of the beam of radiation propagatingthrough the folded path of the sample cell. In one embodiment, anabsorption profile for the trace gas can be determined from aninterference signal propagating through the sample in the sample region.

Other aspects and advantages of the invention will become apparent fromthe following drawings, detailed description, and claims, all of whichillustrate the principles of the invention, by way of example only.

BRIEF DESCRIPTION OF THE DRAWINGS

The advantages of the invention described above, together with furtheradvantages, may be better understood by referring to the followingdescription taken in conjunction with the accompanying drawings. In thedrawings, like reference characters generally refer to the same partsthroughout the different views. The drawings are not necessarily toscale, emphasis instead generally being placed upon illustrating theprinciples of the invention.

FIG. 1 depicts a block diagram of an exemplary detection system formonitoring and/or detecting a trace gas in a gas sample according to theinvention.

FIG. 2 shows a schematic diagram of an exemplary optical configurationaccording to the invention.

FIG. 3 shows a block diagram of an exemplary flow system for introducinga sample into a sample cell according to the invention.

FIG. 4 is a graph of pathlength/NEA versus number of passes betweenoptical surfaces of a sample cell according to the invention.

FIG. 5 is a graph of concentration of a trace gas versus time duringinput of the trace gas into an exemplary detection system according tothe invention.

FIG. 6 shows a timeline for a series of measurements according to theinvention.

FIG. 7 depicts a plan view of an exemplary detection for monitoringand/or detecting a trace gas in a gas sample according to the invention.

FIG. 8 shows a plan view of some components of an exemplary detectionfor monitoring and/or detecting a trace gas in a gas sample according tothe invention.

FIG. 9 shows a flowchart depicting a method for monitoring siloxanecompounds in a biogas, according to an illustrative embodiment of theinvention.

FIG. 10 shows a representation of NIPALS decomposition of spectralinformation represented by matrix X (spectral measurements) and matrix Y(concentration data), according to an illustrative embodiment of theinvention.

FIG. 11 shows individual absorption spectra used to monitor siloxanecompounds in a biogas, according to an illustrative embodiment of theinvention.

DESCRIPTION OF THE INVENTION

FIG. 1 shows a block diagram of an exemplary apparatus 10 for monitoringand/or detecting a trace gas in a gas sample. The apparatus 10 can beused to detect trace amounts of substances such as sarin, tabun, soman,sulfur mustard, and VX nerve gas. The apparatus 10 can also be used, forexample, to detect levels of siloxanes in a biogas. In some embodiments,vapors of a solid or liquid substance can be detected. The apparatus 10can be an absorption spectrometer and/or can be a Fourier TransformInfrared (FTIR) spectrometer. In the embodiment illustrated, theapparatus 10 includes a source 14, an interferometer 18, a sample cell22, a source for a gas sample 26, a detector 30, a processor 34, adisplay 38, and a housing 42. In various embodiments, the apparatus 10can be used to detect a trace amount of a gas in a short period of timewith few, if any, false positives or negatives.

In various embodiments, the source 14 can provide a beam of radiation(e.g., an infrared beam of radiation). The source 14 can be a laser oran incoherent source. In one embodiment, the source is a glowbar, whichis an inert solid heated to about 1000° C. to generate blackbodyradiation. The glowbar can be formed from silicon carbide and can beelectrically powered. The spectral range of the system can be betweenabout 600 cm⁻¹ and about 5000 cm⁻¹. The resolution of the system can be2 cm⁻¹ and about 4 cm⁻¹. In one embodiment, the detection system canrecord a higher resolution spectrum of a trace gas upon detection of thetrace gas. The higher resolution spectrum can aid identification of thetrace gas.

In various embodiments, the source 14 of radiation and theinterferometer 18 can comprise a single instrument. In some embodiments,the interferometer 18 is a Michelson interferometer, commonly known inthe art. In one embodiment, the interferometer 18 is a BRIKinterferometer available from MKS Instruments, Inc. (Wilmington, Mass.).A BRIK interferometer can include a combiner, which splits and combinesincoming radiation, a moving corner cube to modulate the radiation, awhite light source used to identify the center burst, and a VerticalCavity Surface Emitting Laser (VCSEL) to monitor the velocity of thecorner cube. The BRIK interferometer can be immune to tilt and lateralmotion errors, as well as to thermal variations, which can enhance theruggedness of the interferometer.

In one embodiment, the interferometer 18 can be a module including asource of radiation, a fixed mirror, a movable mirror, an optics module,and a detector module (e.g., the detector 30). The interferometer modulecan measure all optical frequencies produced by its source andtransmitted through a sample (e.g., the sample 26 contained within thesample ell 22). Radiation is directed to the optics module (e.g., abeamsplitter), which can split the radiation into two beams, a firstsignal and a second signal. The movable mirror creates a variable pathlength difference between these two initially, substantially identicalbeams of electromagnetic energy. The movable mirror is normally moved orswept at a constant velocity. After the first signal travels a differentdistance (in this embodiment, due to movement of the movable mirror)than the second signal, the first and second signals can be recombinedby the optics module, producing a radiometric signal with an intensitythat is modulated by the interference of the two beams. Thisinterference signal is passed through the sample and measured by thedetector. The presence of different samples (e.g., a solid, liquid, orgas) can modulate the intensity of the radiation as detected by thedetector. The output of the detector is, therefore, a variable,time-dependent signal depending upon the optical path differenceestablished by the relative positions of the fixed mirror and themovable mirror, as well as the modulation of the electromagnetic signalproduced by the sample. This output signal can be described as aninterferogram.

The interferogram can be represented as a plot of received energyintensity versus position of the movable mirror. Those skilled in theart refer to the interferogram as a signal that is a function of time.The interferogram is a function of the variable optical path differenceproduced by the movable mirror's displacement. Since the movablemirror's position is normally and desirably swept at a constantvelocity, those skilled in the art refer to the interferogram as a “timedomain” signal. The interferogram can be understood to be a summation ofall the wavelengths of energy emitted by the source and passed throughthe sample. Using the mathematical process of Fourier Transform (FT), acomputer or processor can convert the interferogram into a spectrum thatis characteristic of the light absorbed or transmitted through thesample. Because individual types of molecules absorb specificwavelengths of energy, it is possible to determine the molecule(s)present in the sample based on the interferogram and the correspondingspectrum. In a similar manner, the magnitude of the energy absorbed byor transmitted through the sample can be used to determine theconcentration of a molecule(s) in the sample.

In various embodiments, an interferometer is not used to form aninterference signal. An absorption spectrometer is used to record anoptical signal, and information about the trace species is derived fromthe signal transmitted through the sampling region. For example, anabsorption spectrum or a differential spectrum can be used.

In various embodiments, the sample cell 22 can be a folded path and/or amultiple pass absorption cell. The sample cell 22 can include analuminum housing enclosing a system of optical components. In someembodiments, the sample cell 22 is a folded-path optical analysis gascell as described in U.S. Pat. No. 5,440,143, the disclosure of which isherein incorporated by reference in its entirety.

In various embodiments, the source of the sample of gas 26 can beambient air. The sample cell 22 or a gas sampling system can collectsurrounding air and introduce it to a sampling region of the sample cell22. The sample of gas can be introduced to the sample cell 22 at apredetermined flow rate using a flow system including an inlet 46 and anoutlet 50 of the sample cell 22.

In various embodiments, the detector 30 can be an infrared detector. Insome embodiments, the detector 30 is a cooled detector. For example, thedetector 30 can be a cryogen cooled detector (e.g., a mercury cadmiumtelluride (MCT) detector), a Stirling cooled detector, or a Peltiercooled detector. In one embodiment, the detector is a deuteratedtriglycine sulfate (DTGS) detector. In one embodiment, the detector is a0.5 mm Stirling-cooled MCT detector with a 16-μm cutoff, which canprovide the sensitivity required for detecting a trace gas. The relativeresponsitivity (i.e., ratio of responsitivity as a function ofwavelength) of the Stirling-cooled MCT detector is at least 80%throughout the main wavelength region of interest (e.g., 8.3-12.5 μm).In addition, the D* value of the Stirling-cooled MCT detector can be atleast 3×10¹⁰ cm Hz^(1/2) W⁻¹. The D* can be defined as the inverse ofthe detector noise equivalent power multiplied by the square-root of theactive element area.

The processor 34 can receive signals from the detector 30 and identify atrace gas by its spectral fingerprint or provide a relative or absoluteconcentration for the particular material within the sample. Theprocessor 34 can be, for example, signal processing hardware andquantitative analysis software that runs on a personal computer. Theprocessor 34 can include a processing unit and/or memory. The processor34 can continuously acquire and process spectra while computing theconcentration of multiple gases within a sample. The processor 34 cantransmit information, such as the identity of the trace gas, a spectrumof the trace gas, and/or the concentration of the trace gas, to adisplay 38. The processor 34 can save spectrum concentration timehistories in graphical and tabular formats and measured spectrum andspectral residuals, and these can be displayed as well. The processor 34can collect and save various other data for reprocessing or review at alater time. The display 38 can be a cathode ray tube display, lightemitting diode (LED) display, flat screen display, or other suitabledisplay known in the art.

In various embodiments, the housing 42 can be adapted to provide adetection system that is one or more of portable, rugged, andlightweight. The housing 42 can include a handle and/or can be readilysecured to a transport mechanism, such as a pullcart or handtruck. Thehousing 42 can be rugged enough to resist misalignment of optics orbreaking of components if transported and/or dropped. In variousembodiments, the apparatus 10 can weigh as little as 40 pounds. In oneembodiment, the apparatus 10 is entirely self-contained (e.g., includesall components in the housing 42 necessary to collect a sample, record aspectrum, process the spectrum, and display information relating to thesample).

FIG. 2 shows an illustrative embodiment of an optical configuration thatcan be used with the apparatus 10. Radiation from the source 14 (e.g., aglowbar) is directed to the interferometer 18 (e.g., including apotassium bromide beamsplitter) by a first mirror 52. The beam ofradiation is directed by a parabolic mirror 54 (PM) to a first foldingmirror 58, and into the sample cell 22. The beam of radiation exits thesample cell and is directed by a second folding mirror 62 to a ellipticmirror 66 (EM), which directs the beam of radiation to the detector 30.

In one representative embodiment, the parabolic mirror 54 has aneffective focal length of about 105.0 mm, a parent focal length of about89.62 mm, and can have an off center value of about 74.2 mm. Thediameter of the parabolic mirror 54 can be about 30.0 mm, and the angleof reflection can be about 45°.

In one embodiment, the elliptic mirror 66 can have a major semi axis ofabout 112.5, a minor semi-axis of about 56.09, and a tilt angle of theellipse of about 7.11°. The diameter of the elliptic mirror 66 can beabout 30.0 mm, and the angle of reflection (chief ray) can be about 75°.

In various embodiments, the first folding mirror 58 can have a diameterof about 25 mm, and the second folding mirror 62 can have a diameter ofabout 30 mm.

The mirrors and optics can include a gold coating, a silver coating, oran aluminum coating. In one embodiment, the elliptic and parabolicmirrors are coated with gold, and the flat folding mirrors are coatedsilver.

In various embodiments, the sample cell can include an objective surface74 and a field surface 78. The objective surface 74 can be substantiallyspherical and concave. The field surface 78 can be concave, andpositioned in a confronting relationship to the objective surface 74.The objective surface 74 can include at least one cylindrical componentincreasing coincidence of foci in at least one plane to maximizethroughput of a beam of radiation propagating between the surfaces 74and 78. In one embodiment, the objective surface 74 can include aplurality of substantially spherical, concave reflective objectivesurfaces, and each surface can include a cylindrical componentincreasing coincidence of foci in at least one plane to maximizethroughput of the beam of radiation. The center(s) of curvature of theobjective surface(s) can be positioned behind the field surface 78. Byincreasing coincidence of focus in at least one plane, distortion,astigmatism, spherical aberration, and coma can be better controlled,and higher throughput can be realized. Adding the cylindrical componentcan serve to reduce the effective radius of curvature in one plane, thusenabling light incident on the reflective surface to better approach thefocus in the orthogonal plane. In one embodiment, the objective surface74 has a cylindrical component superimposed thereupon providingdifferent radii of curvature in two orthogonal planes. The objectivesurface 74 can have a contour that approaches toroidal.

The total pathlengths of the sample cell 22 can be between about 5 m andabout 15 m, although longer and shorter pathlengths can be useddepending on the application. In one detailed embodiment, the samplecell 22 has a total pathlength of about 10.18 m, resulting from a totalnumber of passes of about 48 between the objective surface 74 and thefield surface 78. The optics of the sample cell 22 can be optimized for0.5-mm detector and a 1 steradian collection angle. The detector opticmagnification ratio can be about 8:1. The objective surface 74 and thefield surface 78 can have a gold coating with a nominal reflectance ofabout 98.5% between 800-1200 cm⁻¹. The internal volume of the samplecell can be between about 0.2 L and about 0.8 L, although larger andsmaller volumes can be used depending on the application. In onedetailed embodiment, the volume is about 0.45 L.

In one embodiment, the mirrors and optics used to direct the beam ofradiation into and through the sample cell 22, to focus the beam ofradiation on an entrance slit of the sample cell 22, and/or to directthe beam of radiation to the detector can be optimized to match thesample cell's optical characteristics, which can maximize throughput ofradiation and enhance sensitivity of the detection system.

For example, in one embodiment, an optical configuration properlyaligned can have an efficiency of about 88.8%. As used herein, theefficiency can be the ratio of number of rays impinging the image squareto the total number of emitted rays within the angular range ofemission. In one embodiment, the position of the folding mirrors 58 and62 and the detector 30 can be adjustable, which allows one to compensatefor various mechanical tolerances errors between the interferometer 18,the parabolic mirror 54, the sample cell 22, and the detector 30. In oneembodiment, the following nominal (designed) optical distances can beused to optimize throughput.

Detector to elliptic mirror (X1) of about 21.39 mm.

Elliptic mirror to folding mirror (X2) of about 132.86 mm.

Folding mirror to sample cell (surface of the field mirror) (X3) ofabout 70.00 mm.

Sample cell path length of about 10181.93 mm.

Sample cell to folding mirror (X4) of about 70 mm.

Folding mirror to parabolic mirror (X5) of about 35 mm.

FIG. 3 shows an illustrative embodiment of an exemplary flow system 82for introducing a sample to the sample cell 22. The flow system 82includes a filter 86, a flow sensor 90, an optional heating element 94,the gas cell 22, a pressure sensor 98, a valve 102, and a pump 106connected by gas lines 110. Arrows show the direction of flow. One ormore of the flow system 82 components can include wetted parts, such as,for example, Teflon, stainless steel, and Kalrez, to withstanddecontamination temperatures and to resist the corrosive nature of CWAsand TICs.

The filter 86 can be an inline 2 μm stainless steel filter availablefrom Mott Corporation (Farmington, Conn.). The flow sensor 90 can be amass flow sensor including stainless steel wetted parts, e.g., a flowsensor available from McMillan Company (Georgetown, Tex.). The heatingelement 94 can be line heaters available from Watlow ElectricManufacturing Company (St. Louis, Mo.). The pressure sensor 98 can be aBaratron pressure sensor available from MKS Instruments (Wilmington,Mass.). The valve 102 can be stainless steel and include a Teflono-ring, e.g., a valve available from Swagelok (Solon, Ohio). The gaslines 110 can be ⅜″ diameter tubing available from Swagelok.

The pump 106 can be a “micro” diaphragm pump with a heated head. ADia-Vac B161 pump available from Air Dimensions, Inc. (Deerfield Beach,Fla.) can be used. In one embodiment, a miniature diaphragm pumpavailable from Hargraves Technology Corporation (Mooresville, N.C.) canbe used. In the illustrative embodiment, the pump 106 can be positioneddownstream from the sample cell 22 to draw air through it. As a result,any leakage in the system can be pulled away from, instead of pushedinto, the analyzer to minimize the risk of contaminating the internalcomponents of the analyzer. In addition, an unwanted product of anunintended chemical reaction involving elastomers of the pump can beprevented from entering the sample cell 22.

In various embodiments, the rate of flow through the flow system 82 canbe between 2 L/min and 10 L/min, although larger and smaller flow ratescan be used depending on the application. In one embodiment, the flowrate is between 3 L/min and 6 L/min. The pressure of the sample can beabout 1 atm, although larger and smaller pressures can be maintaineddepending on the application. In some embodiments, the sample cell canbe operated an elevated pressures, such as up to 4 atm. The operatingtemperature of the sample cell can be between about 10° C. and of about40° C., although larger and smaller temperatures can be maintaineddepending on the application. In one embodiment, the detection systemcan include a heating element to heat the sample to between about 40° C.and of about 180° C. In one embodiment, the temperature can be increasedup to about 150° C. to decontaminate the apparatus.

In various embodiments, the sample cell pathlength can be between about5 m and about 12 m. The spacing between the field surface and theobjective surface can be constrained by the gas sampling flow rate. Inone embodiment, a 5.11-meter sample cell with 16 cm spacing and 32passes can have an internal volume of about 0.2 L. In anotherembodiment, For the same number of passes, a 20.3 cm spacing with 32passes can have a volume of about 0.4 L. In yet another embodiment, a25.4 cm spacing can have a volume of about 0.6 L. A flow rate can bedetermined that can provide an adequate supply of “fresh” ambient gas atleast every 10 seconds, although smaller sampling rates can be attained.In various embodiments, the rate of flow (e.g., between 2 L/min and 10L/min) can be optimized to provide an optimal exchange rate of gas. Forexample, in one embodiment, the exchange rate of gas is at least 80% ina detection time interval of 20 seconds. In one embodiment, the gasexchange rate of is between about 80% and about 95% in a detection timeinterval of 10 seconds.

Pathlength/NEA ratio can be used as a metric for quantifying a detectionsystem's sensitivity, where pathlength is the total beam path length ofthe sample cell measured in meters and NEA is the noise equivalentabsorbance measured in absorbance units (AU). Provided that thesensitivity is limited by detection system's non-systematic errors (alsocalled random noise, such as detector and electronic noise), thedetection limit can be inversely proportional to the Pathlength/NEAratio. For example, if the ratio were doubled, the detection limit of aparticular sample in ppb or mg/m³ would be halved. It is thus anappropriate quantification metric for the sensitivity performance. Thismetric does not take into account sensitivity enhancement due toadvanced sampling techniques, such as, for example, gas pressurizationand cold trapping.

Taking into account the limiting system noise, such as detector anddigitization noise, Pathlength/NEA ratio can be optimized for varioussystem configurations. Parameters that can be optimized include flowrate, sample cell volume, optical pathlength, number of passes throughthe sample cell, optical configuration, mirror reflectivity, mirrorreflective material, and the detector used. For example, an optimumdetector is one that has the highest D* value and speed (lower responsetime), within the constraints of size, cost and service life.

For a detector noise limited spectrometer, the sensitivity orPathlength/NEA ratio is proportional to the D* value. Detector bandwidthcan determine the maximum scan speed, which in turn determines themaximum number of data averaging that can be performed within theallowed measurement period. For a detector or electronic noise limitedsystem, sensitivity generally increases with the square root of thenumber of averaged scans or, for example, the time to perform thesescans. In one embodiment, a Stirling-cooled detector can provide aPathlength/NEA sensitivity ratio of at least 1.5×10⁵ m/AU. A DTGSdetector can provide an inexpensive alternative due to its low cost andmaintenance-free life, although it can have a lower D* value and beslower.

The Pathlength/NEA value can be determined by optimizing the distancebetween the field surface and the objective surface and the number ofpasses between these surfaces. FIG. 4 shows a graph of Pathlength/NEA asa function of mirror reflections for various surface spacings, e.g., 6.3inches (16.0 cm), 8 inches (20.3 cm) and 10 inches (25.4 cm). As shownin FIG. 4, the maximum Pathlength/NEA values occur at about 92 passes.At 92 passes, only 25% of the light is transmitted due to reflectionlosses at the mirror surfaces, however. In one detailed embodiment, asample cell has a transmittance of between about 50% and about 60%. Withmirror reflectance of 98.5%, a 60% transmittance corresponds to about 32passes, which is represented by the vertical line in FIG. 4. A 50%transmittance corresponds to about 48 passes. Table 1 shows exemplarycombinations of parameters for providing a sampling system for detectinga trace gas in a sample.

TABLE 1 Exemplary combinations of parameters for providing a samplingsystem for detecting a trace gas in a sample. Pathlength/ Cell Flow FlowSurface Number Total NEA volume rate¹ rate² System spacing (cm) ofpasses pathlength (m) (m/AU) (L) (L/m) (L/m) A 16.0 32 5.11 1.4 × 10⁵0.2 2 3 B 20.3 32 6.5 1.8 × 10⁵ 0.4 4 6 C 25.4 32 8.1 2.3 × 10⁵ 0.6 6 9D 16.0 48 7.7 1.9 × 10⁵ 0.3 3 4.5 E 21.1 48 10.18 2.5 × 10⁵ 0.5 5 7.5 F25.4 48 12.2 3.0 × 10⁵ 0.8 8 12 ¹Flow rate for a gas exchange rate of80% at an interval of 10 seconds. ²Flow rate for a gas exchange rate of90% at an interval of 10 seconds.

The Pathlength/NEA ratio can be translated to detection limits in mg/m³or parts per billion (ppb) of concentration. A method used for such atranslation is a comparison between the expected peak absorbancemagnitude and the expected NEA value. The apparatus 10 can be used todetect trace amounts of a substance such as sarin, tabun, soman, sulfurmustard, and VX nerve gas with a concentration lower than about 500 ppb.In various embodiments, the concentration can be between about 10 ppband about 500 ppb, although higher and lower concentrations can bedetected depending on the system and the application. In someembodiments, the concentration can be between 5 ppb and about 50 ppb,depending on the species. For example, the apparatus 10 is capable ofdetecting a trace amount of sarin with a concentration of between about8.6 ppb and about 30 ppb; a trace amount of tabun with a concentrationof between about 12.9 ppb and about 39 ppb; a trace amount of tabun witha concentration of between about 7.3 ppb and about 22.8 ppb; a traceamount of sulfur mustard with a concentration of between about 36.7 ppband about 370.6 ppb; or a trace amount of VX nerve gas with aconcentration of between about 12.9 ppb and about 43.9 ppb.

Gas renewal rate, which is a measure of the build-up of a fresh gassupply in a sample cell, can be coupled with the Pathlength/NEA ratio,resulting in a detection system response time specified as “X mg/m³ (orppb) of gas Y detected in Z seconds”. The detection system response timeincludes the measurement time and the computation time (e.g., about 5seconds). Table 2 shows exemplary detection system response times forvarious agents such as sarin, tabun, soman, sulfur mustard, and VX nervegas.

TABLE 2 Exemplary detection system response times for trace gasesmeasured using a detection system of the invention. Response ResponseResponse Response time for time for time for time for Trace gas 10 ppb20 ppb 30 ppb 50 ppb Sarin 15.4 12 8.7 7.5 Tabun 22.6 12.6 10.2 8.4Soman 13.7 9.6 8.3 7.2 Sulfur mustard 60 37.5 21.4 13.8 VX nerve gas22.6 12.6 10.2 8.4 All response times are in seconds.

FIG. 5 is a graph of concentration of a trace gas versus time using astep profile input (e.g., the trace gas enters the sample cell at thebeginning of the measurement cycle). The measurement period “A” is thetime when data is collected and/or an interferogram is recorded. Thecomputation period “B” is when the interferogram is converted to aspectrum, and a spectral analysis is performed to produce data fromwhich alarm levels and/or concentration values can be determined.

FIG. 6 shows a timeline for a series of measurements. Agent 1 enters thesample cell and is detected during measurement period 1. Aninterferogram is analyzed during computation period 1. Agent 2 entersthe sample cell during measurement period 1. If agent 2 is sufficientlystrong, it can be detected during the remaining portion of measurementperiod 1. If agent 2 is not detectable, then it is detected during asubsequent measurement period, e.g., measurement period 2, and aninterferogram is analyzed during the succeeding computation period,e.g., computation period 2.

In one embodiment, readings can be separated temporally with a fixedpredetermined interval. In various embodiments, the interval can bebetween about 1 second and about 1 minute, although smaller or largerintervals can be used depending on the application. In some embodiments,the interval is about 5 seconds, about 10 seconds, or about 20 seconds.The response time, therefore, depends on this interval as well as whenthen agent is detectable by the detection system.

In various embodiments, the detection system can adapt one or moreparameters based on an external factor, such as detection of a tracegas, a threat level, the time of day, the number of people in a room orbuilding that can be affected by the agent, a particular measurementapplication or scenario, or a combination of the aforementioned. Forexample, in a high-threat condition, a smaller interval can be used tominimize detection time and maximize detectability of a trace agent. Ina low threat situation, a larger interval can be used, which canpreserve the detection systems lifetime and reduce the likelihood of afalse alarms (either false positives or false negatives).

Furthermore, an individual measurement that exceeds a threshold levelfor a particular agent can trigger the detection system to decrease theinterval so that additional measurements can be made in a shorter amountof time. In various embodiments, a first spectrum can be recorded at afirst resolution or sensitivity. If a contaminant is detected, a secondspectrum can be recorded at a higher resolution or sensitivity,respectively. Furthermore, the detector can have a standby mode, inwhich it operates at a higher temperature, thereby decreasing itssensitivity. When triggered by the external factor, the temperature ofthe detector can be decreased to improve its sensitivity.

In various embodiments, the detection system can change the number ofscans based on an external factor or a perceived threat. For example, anincreased number of scans can be performed to enhance the sensitivity ofthe detection system. In one embodiment, the detection system canoperate at higher resolution while recording these additional scans. Inone embodiment, each scan can include an increased number of averages orindividual scans.

In various embodiments, the detection system only digitizes a lowfrequency region (e.g., lower than 1300 cm⁻¹) of the spectrum, so thatthe detection system can scan at a faster rate. An electronic filter ordetector response function can be used to remove a higher frequencyregion (e.g., greater than 1300 cm⁻¹) so that aliasing can be preventedor minimized.

In some embodiments, the detection system can detect the presence of atrace gas in one portion of the spectrum. A second portion of thespectrum can be analyzed to confirm the presence of the trace gas and/ordetermine the trace gas's concentration level.

In one embodiment, the detection system can be packaged as a compact,self-contained multiple gas analyzer. For example, the detection systemcan be a diagnostic tool for recording, charting, analyzing, andreporting air quality. FIGS. 7 and 8 shows an exemplary detection systemfor monitoring air quality, e.g., ambient air for trace gases. Referringto FIG. 7, the detection system includes a housing 42′, a first display38′, a second display 38″, a gas inlet 46′, a gas outlet 50′, and a port118 for connecting to external devices.

The housing 42′ can be a three-dimensional rectangular box including atop panel 122, side panels 126, and a bottom panel 130 (shown in FIG.8). The top panel 122 can be hinged off a side panel 126, so that thehousing 42′ can be opened for service. The external surface of the toppanel 122 can include the first display 38′ and the second display 38″attached thereto or embedded therein. The first display 38′ can be aliquid crystal display (LCD), for example, with a touchscreen display.The first display 38′ can receive commands for operating the detectionsystem and can display a graphical user interface (GUI). The seconddisplay 38″ can be a light emitting diode (LED) display, for example,with a series of LEDs that light up to indicate a threat level, alarmstatus, and/or detection system health status. For example, the seconddisplay 38″ can include a first series of green, yellow and red LEDs toindicate an alarm status, and a second series of green, yellow and redLEDs a separate to indicate sensor health status. In variousembodiments, the housing 42′ can define a hole for intake of ambientair. The hole can be used to introduce the sample of gas into the flowsystem for detection in the sample cell.

FIG. 8 shows internal views of the top panel 122 and the bottom panel130 when the top panel 122 is hinged open. The bottom panel includes aninternal chassis including an optics box 134 for housing opticalcomponents. The optics box 134 can be formed from an aluminum shell(e.g., 6061-T6). In one embodiment, the optics box 134 is a hermeticallysealed box. As illustrated in FIG. 8, the optics box 134 includes asource 14′, an interferometer 18′, a sample cell 22′, a detector 30′, aparabolic mirror 54′, a first folding mirror 58′, a second foldingmirror 62′, an elliptic mirror 66′, an objective surface 74′, and afield surface 78′. The optics box 134 also can include a flow systemincluding a valve 138 to regulate gas flow, a pressure sensor 98′, apump 106′, and gas lines 110 and fittings 142 for making connections.Power supplies 146 for various components and a fan 150 can also beattached to the bottom panel 130. The detection system can be operatedin still air, and fans 150 can maintain the internal temperature of thesystem. The bottom panel 130 also includes a connector 154 to interfacewith the top panel 122.

As illustrated in FIG. 8, the top panel 122 can include electroniccomponents attached thereto. For example, the top panel 122 can includea data acquisition module 158, a mirror motion control module 162, asingle board computer 166, a power distribution module 170, and a harddrive 172. The data acquisition module 158 can include a preamplifier,an analog-to-digital converter, and a data acquisition board. Thepreamplifier can amplify an analog signal received from the detector30′. The analog signal can be converted to a digital signal using theanalog-to-digital converter. The data acquisition board can be aNetburner processor board available from Netburner (San Diego, Calif.).The single board computer 166 can be an off the shelf PC motherboardrunning Windows and presenting a GUI to a user.

The power distribution module 170 can handle and distribute power toother modules in the system, and can implement health and status sensorsused to monitor the detection system's functionality. For example, thepower distribution module 170 can distribute AC power to system powersupplies 146 and fans 150, and can control temperature controllers 174,e.g., Love Controls available from Dwyer Instruments, Inc. (MichiganCity, Ind.). The power distribution module 170 also monitors sample cellpressure, differential pressure across the air filter, sample celltemperature, and detector temperature, A/D converts the outputs, andcommunicates the results back to the single board computer 166. Thepower distribution module 170 also can control a Stirling cooleddetector's cooler motor under command from the single board computer166. The top panel 122 also can include sample cell temperaturetransmitter.

Data processing can be performed using the modules attached to the toppanel 122, which can enable real time analysis of data. The spectrallibrary can include spectral fingerprints of between about 300 and about400 gases, although more gases may be added as spectra are recorded.Data processing can be performed with a standard computer programminglanguage, such as MATLAB or C++. The spectra recorded can be transferredto MATLAB for spectral post-processing to compute gas concentrations,spectral residuals, and/or false alarm rates. In various embodiments,the detection system can operate with fewer than about six false alarmsper year. False alarms can result from noise, anomalous spectraleffects, analysis code, model errors, errors in spectral library, or anunknown interferent.

The computer software can operate on a Java based platform withgraphical remote control capability. It can incorporate standardservices including user login, web-based GUI, alarm triggering, and/oran Ethernet interface to a client computer that may be located remotefrom the detection system. The computer software can perform remotehealth and control diagnostics. In addition, the port 118 can be used toconnect the system to a stand alone computer, which can perform dataprocessing and data analysis.

The housing 42′ is designed to withstand a 50 G shock. In oneembodiment, the housing 42′ can have a length of about 406 mm and awidth of about 559 mm. The mass the detection system can be about 20 kg.The housing 42′ can be mountable on a wall, on a movable cart, or on ahandtruck, and can include a handle (not shown) for carrying, eithermanually or using a mechanical lifting apparatus. In one embodiment, thehousing can be mounted as part of an air handling system for a building.When the detector senses the presences of a contaminant, remedialmeasures can be taken to account for the contaminant. For example, analarm can sound to evacuate the building, or air flow in the airhandling system can be increased to sweep the contaminant away from apublic area or to dilute the trace gas to an acceptable level.

In various embodiments, the detection system can be operated at anelevated temperature to decontaminate the system in the event ofcontamination. The system can be configured so that the sample cell andflow system can be heated to a temperature of between about 150° C. andabout 200° C., while the remaining components including electronics andoptical components are maintained at a temperature below about 70° C.For example, the components being heated to about 150° can be insulatedfrom the surrounding components to prevent damage of electronics andrealignment or damage of optical components. Operation of the samplecell and flow system at an elevated temperature can speed up desorptionof the contaminant. In one embodiment, the detection system can beoperated while the system is being decontaminated, so that progress ofthe decontamination can be monitored. In one embodiment, the detectionsystem is purged with nitrogen gas or ambient air duringdecontamination. The gas can include moisture (e.g., a relative humidityof greater than or equal to about 30%). In various embodiments, thesystem can be decontaminated in less than about 2 hours and be ready tobe returned to service.

In one embodiment, a concentration of a contaminant in a detectionsystem can be determined, and if the concentration of the contaminantexceeds a contamination value, at least the sample region can be heatedto a decontamination temperature to remove the contaminant. Theconcentration of the contaminant can be monitored while heating thesample region, and when the concentration of the contaminant reaches adecontamination value, the heating can be abated or ceased. Thecontamination value can be a concentration of a substance that inhibitsthe performance of the detection system. The decontamination value canbe a concentration of the substance at which the detection system can beoperated without influence from the contaminant.

In various embodiments, the sample cell of the detection system can beoperated at elevated pressure. Although the Pathlength/NEA ratio may notchange, the sensitivity of the detection system can be enhanced as alarger amount of a trace gas sample can be present in a sample cellhaving the same pathlength. This, in turn, can generate a largerabsorption signal, relative to the baseline. The pressure can beelevated by increasing the flow rate while keeping the sample cellvolume unchanged.

The field surface and the objective surface can be fixably mounted sothat their position remains substantially unchanged when the pressure iselevated. For example, the field surface and the objective surface canbe mounted on rods to hold these surfaces. In addition, the sample cellcan be substantially air tight. The objective surface and the fieldsurface in the sample cell can be bathed in the sample gas so that apositive pressure can be applied to a back surface of each of the fieldsurface and the objective surface to prevent deformation at elevatedpressure. In various embodiments, the pressure can be between 1 atm andabout 10 atm. In one embodiment, the pressure is 4 atm.

In some embodiments, signals at two distinct pressures can be measuredand a ratio of these signals can be taken. The ratio of signals canremove baseline noise, enhance sensitivity, and/or increase theamplitude of the absorption profile of the trace gas relative to thebaseline signal.

A first signal of a beam of radiation propagating through a sample ofambient air at a first pressure in the sample cell is measured. Thesample cell is pressurized with ambient air to a second pressure. Asecond signal of the beam of radiation propagating through the sample ofambient air is measured at the second pressure in the sample cell. Thefirst signal and the second signal can be combined to determine a signalindicative of the presence of a trace gas. For example, the signals canbe combined to yield an absorption profile for the trace gas. In oneembodiment, the beam of radiation can include an interference signal.The absorption profile for the trace gas can be determined from theinterference signal. In one embodiment, the first pressure is about 1atm, and the second pressure is between about 1 atm and 10 atm. In onedetailed embodiment, the first pressure is about 1 atm, and the secondpressure is about 4 atm.

In various embodiments, the first signal is used as a baseline signalfor the second signal because the optical alignment of the sample cellremains substantially unchanged when the pressure is increased. In someembodiments, a baseline signal is measured and used as the baselinesignal for both the first signal and the second signal.

In various embodiments, the flow system can include a cold finger totrap a gaseous sample of interest by cooling it down below itssaturation temperature. Many volatile materials condense at or below atemperature of −75° C. In one embodiment, a cryogenic cold trap isestablished in the gas outlet from the sample cell. After a specifiedperiod of time or collection period, a trapped gas or trapped gases canbe rapidly vaporized or “flashed” back into the sample cell by heatingthem up, and a spectral measurement can be made. This technique canincrease the amount of a target gas by about an order of magnitude ortwo, while maintaining the sample cell at atmospheric pressure. In oneembodiment, continuous flow measurements are performed after an intervalof time, e.g., about every 10 seconds, while flashing occurs at a longertime interval.

In various embodiments, the detection system can include along-wave-pass filter. Noise due to the A/D converter can be on the sameorder of magnitude with the noise due to the detector. Incorporating along-wave-pass filter can block the higher wavenumber region, and canimprove sensitivity by reducing the digitizer dynamic range requirementthrough reduction of the interferogram centerburst magnitude. Thedynamic range of a detector without an optical filter can be betweenabout 600 cm⁻¹ and about 5000 cm⁻¹. Since many of the toxic substancestargeted are detectable below 1500 cm⁻¹, the spectrum higher than 1500cm⁻¹ can be eliminated using a long-wave-pass filter to gainsensitivity. For example, with a standard off-the-shelf long-wave-passfilter with a cut-off at about 1667 cm⁻¹, the gain in Pathlength/NEAratio can be about 20% to about 30%. In addition, using a long-wave-passfilter can improve a detection system's signal-to-noise ratio by allow adetector to be operated at higher gain, e.g., the highest gainachievable with a particular detector. In various embodiments, a lowsensitivity detector, such as a MCT detector or a DTGS detector, can beused to record a spectrum in a higher frequency region.

Biofuels can be used to power engines for turbine generators. Biogasesgenerally include species such as, for example, hydrocarbons (e.g., CH₄)with percentage levels of CO₂ and H₂O. Biogases also include siloxanecompounds. Cyclic siloxanes (e.g., D3-siloxanes to D6-siloxanes) can befound in biogas produced by a digester. Biogases from landfills caninclude linear siloxanes (e.g., “straight chain” L2-siloxanes toL6-siloxanes). Concentrations of siloxane compounds in biogases canrange from parts per million (ppm) levels down to parts per billion(ppb) levels. Siloxane compounds produce SiO₂ when burned within theturbine, promoting excessive wear and tear. Therefore, continuousmonitoring of biofuel processing system for siloxanes can enable earlydetection and measurement of siloxane compounds. A system can use astand-alone processor (e.g., processor 34 of FIG. 1) to quantifyconcentrations of siloxanes (e.g., a stand-alone FTIR that detects levelof siloxane impurities in biofuels in a range of ppm levels down to ppblevels).

FIG. 9 shows a flowchart depicting an illustrative method for monitoringsiloxane compounds in a biogas. The method includes the step ofproviding a non-absorptive gas (e.g., nitrogen or helium) to a samplecell (e.g., sample cell 33 of FIGS. 1 and 3) (Step 205). Anon-absorptive gas is a gas having substantially no infrared absorptionsin a specified wavelength range of interest. The method also includesthe step of taking a first spectral measurement from the sample cell(e.g., a background instrumental response) (Step 210). A biogas isprovided to the sample cell (Step 215). The biogas includes at least onesiloxane compound (e.g., selected from a group consisting ofL2-siloxane, L3-siloxane, L4-siloxane, L5-siloxane, D3-siloxane,D4-siloxane, D5-siloxane, or D6-Siloxane). The method also includestaking a second spectral measurement from the sample cell (Step 220). Afirst absorption spectrum is generated based on a ratio of the firstspectral measurement from the non-absorptive gas to the second spectralmeasurement (e.g., the measurement from a sample gas comprising thebiogas provided to the sample cell) (Step 225). A second absorptionspectrum is generated based on, at least, a first individual absorptionspectrum for a known concentration of the at least one siloxane compoundin the biogas (Step 230). A concentration of the at least one siloxanecompound in the biogas is calculated by using the first absorptionspectrum and a second absorption spectrum (Step 235). Using, forexample, CLS and/or other methods that do a direct spectral comparison,the concentration of the at least one siloxane compound can becalculated once all of the possible interferences/gases are firstremoved from the spectrum (e.g., the first absorption spectrum).

Both the non-absorptive gas and the biogas can be provided to a samplecell (Steps 205 and 215) so that the spectral measurements can be taken(Steps 210 and 220). The biogas can come from, for example, animalwaste, wastewater or a landfill. Generally, the greater the dataacquisition period (e.g., the period of time for taking spectralmeasurements), the lower the detection limit (e.g., lower concentrationsof species can be detected). A greater data acquisition period allowsfor a more precise measurement (e.g., a larger signal-to-noise). If, forexample, the noise is random (e.g., white noise), the signal-to-noisewill increase with the square root of the acquisition period time. Thesecond spectral measurement (e.g., Step 220) can be taken over anacquisition period of about 10 seconds to about 20 seconds. In someembodiments, the second spectral measurement from the sample cell istaken in a wavelength range of about 8 microns to about 12 microns. Thestep of taking the second spectral measurement can include acquiring aninfrared signal from the sample cell (e.g., taking a sample of a gascomprising the biogas).

The concentration of at least one siloxane compound can be calculated(e.g., Step 235) in real time (e.g., results of quantifyingconcentrations of siloxane compounds obtained in seconds or minutes) andin-situ (e.g., in-line or in a device in fluid communication with asource of the biogas and without the need for grabbing a sample). Sincea sample cell and processor (e.g., processor 34 of FIG. 1) can be placedin fluid communication with the source of the biogas, the analysis canbe done at/substantially near the source, without the need for obtaininga sample and transporting it off site to be analyzed (e.g., as withexisting GC/MS methods). The time to obtain and analyze the sample(e.g., calculate concentrations of siloxanes) can be done on the scaleof seconds to minutes, dependent on the ultimate signal-to-noise neededto accurately quantify the siloxanes at the levels extant in the biogasmixture. If, for example, the signal-to-noise is not sufficient toprecisely measure a particular concentration, then the acquisition timecan be increased to further lower the noise (e.g., to increase thesignal-to-noise). In some embodiments, a turbine generator is shut offwhen the concentration of at least one siloxane compound reaches athreshold value.

In some embodiments, a processor (e.g., processor 34 of FIG. 1) is usedto calculate a concentration of at least one siloxane compound in thebiogas (Step 235). Chemometrics, which combines spectroscopy (e.g., FTIRspectroscopy) and mathematics (e.g., multiple regression analysis), canprovide clear quantitative information for siloxane compounds in thebiogas. For example, the processor is used to perform multipleregression analysis using the first absorption spectrum and the secondabsorption spectrum to calculate the concentration of at least onesiloxane compound in the biogas. Multiple regression analysis can beperformed using Classical Least Squares (CLS), Partial Least Squares(PLS), Inverse Least Squares (ILS), Principal Component Analysis (PCA),and/or other chemometric algorithms.

A second absorption spectrum (e.g., from Step 230) can be generatedbased on, at least, the first individual absorption spectrum andindividual absorption spectra for one or more additional siloxanecompounds (e.g., L2-siloxane, L3-siloxane, L4-siloxane, L5-Siloxane,D3-siloxane, D4-siloxane, D5-Siloxane, or D6-siloxane), hydrocarboncompounds, water or carbon dioxide. The second absorption spectrum canbe a model (e.g., a model representative of the individual absorptionspectra of the agents in the biogas) based on known concentrations ofthe siloxane compounds, hydrocarbon compounds, water or carbon dioxide.In some embodiments, the second absorption spectrum is a model based on,at least, a first individual absorption spectrum (e.g., for a siloxanecompound) and/or individual absorption spectra for one or moreadditional siloxane compounds, hydrocarbon compounds (e.g, methane orethane), water or carbon dioxide.

In some embodiments, a value for the concentration for the at least onesiloxane compound is determined (e.g., Step 235) such that the secondabsorption spectrum is substantially similar to the first absorptionspectrum (e.g., mathematically fitting the model absorption spectrum tothe measured absorption spectrum). By way of example, a concentration ofat least one siloxane compound can be calculated by providing at leastone variable representing the concentration of the at least one siloxanecompound and determining a value for the at least one variable (e.g., avalue for the concentration) such that that the second absorptionspectrum is substantially similar to the first absorption spectrum(e.g., mathematically fitting the second absorption spectrum to thefirst absorption spectrum).

For example, spectral measurements can be directly linked to the actualchemical constituent using a variety of different types of quantitativeanalysis based upon both univariate and multivariate analysistechniques. Univariate methods include correlating spectral peak heightsor areas under the spectral curve to the same characteristics for knownchemical quantities of the species in the biogas. In some embodiments,this can be done using, for example, least squares regression to developa quantitative model that predicts the actual concentrations ofdifferent species in the biogas. Another univariate method that can beused in alternate embodiments is K-Matrix or classical least squares(CLS), which is based on an explicit linear additive model (e.g., Beer'slaw, described in equation 1 below). CLS uses larger sections of thespectra (or the whole spectrum) in a regression with respect to all ofthe chemical components within the spectral region.

CLS has the limitation that it requires the concentrations of allspectrally active components be known and included in the calibrationmodel before an adequate prediction model can be developed because, forexample, unknown concentrations will reduce model accuracy. To avoidthis and other complications that can arise when using univariatemodels, multivariate techniques are typically more useful. In onemultivariate method, multiple linear regression (MLR) (also termedP-Matrix or inverse least squares (ILS)) is used to build a model usingonly the concentrations of the chemical components of interest (see,e.g., H. Mark, Analytical Chemistry, 58, 2814, 1986). A model may bebuilt with this technique using only the known concentration without anyunwanted effects; however, the model is limited in the number ofwavelengths that can be used to describe each of the components.

Other multivariate techniques may be used in alternate embodiments thatcombine the ability to use large regions of the spectra to represent theconstituents of interest (like that of the CLS model) with the abilityof having to contend with only the constituents of interest (like thatof the MLR model). In one embodiment, principal component regression(PCR) is used (as described in Fredericks et al., Applied Spectroscopy,39:303, 1985). This method is based upon spectral decomposition usingprinciple component analysis (PCA), followed by the regression of theknown concentration values against a PCA scores matrix. Specifically,with PCR, a PCA is first made of the X-matrix resulting in a scorematrix T and a loading matrix P. In the next step, a few of the firstscore vectors are used in a multiple linear regression with the Y-data.Where the first few components of PCA really summarize most of theinformation in X related to Y, PCR works nearly as well as partial leastsquares (PLS) for spectroscopic data, which is described below.

In another embodiment, PLS can be used to obtain actual concentrationvalues of lesion constituents based upon spectral data (see, e.g., PGeladi and B Kowalski, Analytica Chemica Acta, 35:1, 1986, and Haalandand Thomas, Analytical Chemistry, 60:1193 and 1202, 1988). PLS issimilar to PCR; however, with PLS both the spectral information and theconcentration information are decomposed at the start of the method andthe resultant scores matrices are swapped between the two groups. Thiscauses the spectral information that is correlated with theconcentration information to be weighted higher within the model, whichcan result in a more accurate model than PCR. The core of the PLSalgorithm is a spectral decomposition step performed via eithernonlinear iterative partial least squares (NIPALS) (see, e.g., Wold,Perspectives in Probability and Statistics, J Gani (ed.)(Academic Press,London, pp 520-540, 1975) or simple partial least squares (SIMPLS)(Jong, Chemom. Intell. Lab. Syst., 18:251, 1993) algorithms.

Further details of PCA, PCR, MLR and PLS analysis can be found in“Multi- and Megavariate Data Analysis, Part I, Basic Principles andApplications”, Eriksson et al, Umetrics Academy, January 2006 and“Multi- and Megavariate Data Analysis, Part II, Advanced Applicationsand Method Extensions”, Eriksson et al, Umetrics Academy, March 2006 theentirety of which are herein incorporated by reference.

As noted above, various chemometric algorithms (e.g., PCA, PCR, MLR,PLS) can be used to calculate concentrations of one or more siloxanecompounds in a biogas. Chemometric algorithm methods are utilized to fitthe overall absorption (e.g., measured spectrum based on spectralmeasurements from the biogas) to the absorptions of each of theconstituent species (e.g., siloxanes) and provide a calculatedconcentration of each. Beer's law states that:

A _(i)({tilde over (v)})=a _(i)({tilde over (v)})bc _(i)  EQN. 1

Where A_(i)({tilde over (v)}) is the absorbance of species i atwavenumber {tilde over (v)}, a_(i)({tilde over (v)}) is the absorptivityof the species at that wavenumber, b is the pathlength and c_(i) is theconcentration of the species. Therefore, by measuring absorbance of aspecies at a known concentration, it is possible to determine theabsorptivity of the species for the known concentration and a givenwavelength (e.g., wavenumber). An absorption spectrum can be generatedby measuring the absorbance of a species, at known concentrations, for arange of wavelengths.

If there are multiple species (e.g., molecules) in a sample, Equation 1can be modified to reflect the fact that a measured absorbance of asample (e.g., a sample biogas in a sample cell) is the sum of theabsorbances of all the species in the sample. By way of example, if abiogas includes one or more siloxane compounds, hydrocarbon compounds,water and carbon dioxide, then the measured absorbance of a biogassample is the sum of all the absorbances of the species in the biogas(e.g., a sum of the siloxane compounds, hydrocarbon compounds, water andcarbon dioxide). Accordingly, a quantitative analysis can be used topredict the actual concentrations of different siloxane compounds in thebiogas.

Chemometric algorithms can be used to determine concentration of speciesin a sample. For example, Chemometric algorithms can be used withEquation 1 and/or other equations to determine values for concentrationssuch that the model spectrum (e.g., the second absorption spectrum) issubstantially similar to the measured spectrum (e.g., the firstabsorption spectrum) (e.g., by mathematically fitting the model spectrumto the measured spectrum once all of the interfering components areremoved).

In one embodiment, PLS is used to calculate the concentration ofsiloxane (and/or other compounds in the biogas). FIG. 10 is a diagramrepresenting the nonlinear iterative partial least squares (NIPALS)decomposition of the spectral information represented by matrix Xcontaining spectral measurements and matrix Y containing concentrationinformation. PLS components of a PLS model are traditionally calculatedusing the NIPALS algorithm (or other similar decomposition algorithms).PLS relates two data matrices, X and Y, to each other by a linearmultivariate model. In summary, a linear model specifies therelationship between a dependent or response variable y or a set ofresponse variables Y, and a set of predictor variables X's. For example,the response variable y is concentration, and the predictor variables Xare the spectral measurements 1002 a through 1002 n. The numbers 1.0 and0.45 in Y are the calculated concentrations for the gas components thatare in the corresponding spectrum. There are many variations on theNIPALS algorithm, which consist of a matrix-vector multiplication (e.g.,X′ y). S and U are resultant scores matrices from the spectral andcomponent information, respectively. The numbers 0.39 and −0.37 in S arethe scalar (score) modifiers for the basis vectors which represent thelinear combination of the original set of spectra. These numbers areexemplary only of the first row of numbers that fill S and U. In thisexample the entire set of observed spectra are decomposed into two basisvectors, which is why there are two numbers. If the corresponding row inthe PCx representation is multiplied by these numbers, the originalspectrum is regenerated (e.g., minimus noise). In other embodiments, theset of observed spectra can be decomposed into any number of basisvectors. PCx and PCy are resultant principal components (or latentvariables/eigenvectors) for the spectral and component information,respectively. PCx includes latent variable 1004 a through 1004 f. Theother nomenclature in the figure is for the number of spectra (n), thenumber of data points per spectra (p), the number of components (m), andthe number of final latent variables/eigenvectors (f).

The first decomposition for the spectral and concentration/constituentdata produces a latent variable and score for each of the X and Ymatrices, the scores matrix for the spectral information (S) is swappedwith the scores matrix containing the concentration information (U). Thelatent variables from PCx and PCy are then subtracted from the X and Ymatrices, respectively. These newly reduced matrices are then used tocalculate the next latent variable and score for each round until enoughlatent variables from PCx and PCy are found to represent the data.Before each decomposition round, the new score matrices are swapped andthe new latent variables from PCx and PCy are removed from the reduced Xand Y matrices.

The final number of latent variables (or basis vectors) determined fromthe PLS decomposition (f) is highly correlated with the concentrationinformation because of the swapped score matrices since swapping scorematricies results in the spectral information being correlated with theconcentration information. Advantageously, swapping leaves behind thebasis vectors in both sets of matricies, which are naturally correlatedto one another. The PCx and PCy matrices contain the highly correlatedvariation of the spectra with respect to the constituents used to buildthe model. The second set of matrices, S and U, contain the actualscores that represent the amount of each of the latent variablevariation that is present within each spectrum. It is the S matrixvalues that are used in the PLS model.

In one embodiment, the PLS method is used to predict that actualcompositions of the siloxane compounds in the biogas. For example thePLS algorithm can be used to predict the chemical content of the biogasdirectly or, for example, in the form of a percentage of compoundspresent (e.g., siloxane compounds, hydrocarbon compounds, water, orcarbon dioxide).

In another embodiment, CLS can be used to calculate the concentration ofsiloxane (and/or other compounds) based on the model spectrum and themeasured spectrum. In some embodiments, a sample includes two componentsand/or species (s_(i) and s₂) in a mixture. A biogas can include morethan two components/species (e.g., for example, the biogas can includedifferent species of siloxanes, hydrocarbons, etc.), however, for thepurposes of clarity, the example below assumes two components.

If a sample includes two species, then the species should vary at, atleast, two wave numbers. In one embodiment, the absorbances of the twowavenumbers can be modeled using CLS based on the relationships forabsorbances at each wavelength. For example, the absorbance of the firstwavelength is based on a relationship of the absorptivity of the firstspecies s₁ at the first wavelength, the absorbance of the second speciess₂ at the first wavelength, the pathlength (e.g., pathlength of thesample cell 22 as described above for FIGS. 2-4), the concentration ofthe first species s₁, the concentration of the second species s₂, andthe residual error yielded from the regression analysis of the firstwavelength. Similarly, the absorbance of the second wavelength, forexample, is based on a relationship of the absorptivity of the firstspecies s₁ at the second wavelength, the absorbance of the secondspecies s₂ at the second wavelength, the pathlength, the concentrationof the first species s₁ and the second species s₂, and the residualerror yielded from the regression analysis of the second wavelength.

If the pathlength is constant, then the pathlength need not beconsidered when determining the absorbance of each wavelength. Instead,the absorbance of the first wavelength is based on a relationshipbetween the absorption coefficients for the first species s₁ at thefirst wavelength, the absorption coefficient of the second species s₂ atthe first wavelength, the concentration of the first species s₁ and thesecond species s₂, and the residual error yielded from the regressionanalysis of the first wavelength. Similarly, the absorbance of thesecond wavelength is based on a relationship between the absorptioncoefficients for the first species s₁ at the second wavelength, theabsorption coefficient of the second species s₂ at the secondwavelength, the concentration of the first species s₁ and the secondspecies s₂, and the residual error yielded from the regression analysisof the second wavelength.

Using the relationships described above, the absorption coefficients canbe determined for a wavelength by measuring the absorbances of a sampleat known concentrations. These absorbance coefficients can then be usedto measure/determine unknown concentrations of species s₁ and s₂ in asample. For example, the absorbances of the sample (e.g., the measuredspectrum) can be measured at the two wavelengths, yielding values forthe absorbances of the wavelength numbers, respectively. Since theabsorbance coefficients are known, they can be used with the absorbancevalues to calculate concentrations for the species.

As noted above, a biogas can include more than two components/species.In such a case, the values for absorbances, absorption coefficients, andconcentration can be modeled using the following matrices:

$\begin{matrix}{{\begin{matrix}A_{1,1} & \ldots & \ldots & \ldots & A_{n,1} \\\vdots & \; & \; & \; & \vdots \\\vdots & \; & \; & \; & \vdots \\A_{1,p} & \ldots & \ldots & \ldots & A_{n,p}\end{matrix}} = {{\begin{matrix}K_{1,1} & \ldots & \ldots & \ldots & K_{m,1} \\\vdots & \; & \; & \; & \vdots \\\vdots & \; & \; & \; & \vdots \\K_{1,p} & \ldots & \ldots & \ldots & K_{m,p}\end{matrix}}{\begin{matrix}C_{1,1} & \ldots & \ldots & \ldots & C_{1,n} \\\vdots & \; & \; & \; & \vdots \\\vdots & \; & \; & \; & \vdots \\C_{m,1} & \ldots & \ldots & \ldots & C_{m,n}\end{matrix}}}} & {{EQN}.\mspace{14mu} 2}\end{matrix}$

where the “A matrix” is a matrix of spectral absorbances, the “K matrix”is a matrix representing absortivity coefficients and the “C matrix” isa matrix representing the concentrations. The number of samples(spectra) is represented by “n”, the number of wavelengths used forcalibrations is represented by “p” and the number of species/componentsis represented by “m”. Equation 6 can be simplified and used tocalculate the concentration of species in a sample:

C=A·K ⁻¹  EQN. 3

where K⁻¹ is the inverse of the K matrix. The K matrix from Equation 2can be solved by measuring absorbances of a sample where theconcentration of the individual species are known and using thefollowing expression:

K=A·C ⁻¹  EQN. 4

If the concentrations of individual species (e.g., siloxane compounds,hydrocarbon compounds, water, or carbon dioxide present in, for example,biogas) are known, the “C matrix” is known. The “A matrix” can beconstructed based on spectral measurements obtained using, for example,the detection system of FIG. 1 (e.g., an FTIR spectrometer). Therefore,using the A matrix and the inverse of the C matrix from the knownconcentrations, Equation 4 is used to determine the K matrix.

Once the K matrix has been calculated from Equation 4, Equation 3 isused to calculate concentrations in a sample. An inverse of the K matrix(e.g., calculated from Equation 4 using samples with knownconcentrations) is used to calculate concentrations of species in asample (e.g., siloxanes in a biogas) where the concentrations of theindividual species are unknown. Spectral measurements from a sample(e.g., a sample biogas) can be obtained using a detection system (e.g.,system in FIG. 1). The A matrix, representing a compilation ofabsorbances of the individual species in the sample, is generated basedon the spectral measurements. The inverse of the K matrix and the Amatrix are used in Equation 3 to calculate concentrations of theindividual species in the sample.

FIG. 11 shows graphical result for a CLS (i.e., classical least squares)analysis of wastewater digester gas components including 920 ppbD4-siloxane, 400 ppb D5-siloxane, 65% Methane, 35% carbon dioxide, 1400ppm Ethane, 340 ppm Propane and 65 ppm Butane. The graph shows valuesfor absorbances (y-axis) as a function of wavelength (i.e., wavenumber)(x-axis). Curve 300 represents the measured spectrum, curve 305 is theindividual absorption spectrum for methane, curve 310 is the individualabsorption spectrum for carbon dioxide, curve 315 is the individualabsorption spectrum for ethane, curve 319 is the individual absorptionspectrum for propane, curve 320 is the individual absorption spectrumfor butane, curve 325 is the individual absorption spectrum forD4-siloxane, and curve 330 is the individual absorption spectrum forD5-siloxane. The model absorption spectrum representative of the sum ofthe constituent spectra 305, 310, 315, 320, 325, and 330 has not beenshown for purposes of clarity because due to relatively small residualvalues, the model absorption spectrum would overlay the measuredspectrum 300.

Data such as the spectra showed in FIG. 11 can be used to calculateconcentrations of siloxane compounds (e.g., D4-siloxane andD5-siloxane). Measured/observed spectrum 300 can be used to populatevalues for the A matrix of Equations 7 and 10. Individual absorptionspectra 305, 310, 315, 320, 325, and 330 for the individual species canbe used, for known concentrations, to populate values for the K matrixand/or P matrix of Equations 7 and 10. Accordingly, the measured Amatrix and the calculated K matrix and/or P matrix are used to determinevalues for the unknown concentrations of individual species in themeasured spectrum.

In another embodiment, ILS can be used to calculate concentration ofspecies in a sample. In CLS, absorbance is the dependent variable. InILS, concentration becomes the dependent variable. For example, theconcentration of a first species s₁ is based on a relationship betweenthe linear reciprocal coefficients (which is a function of absorptivityof the first species s₁ at the two wavelength numbers), the absorbancesat the first wavelength and the second wavelength, and the residualerrors yielded from the regression analysis for the first species s₁.This can be simplified to the following matricies when there are severalspecies in a sample:

C=P·A+E _(c)  EQN. 5

In Equation 5, C is a matrix of the concentrations, P is a matrix of thelinear reciprocal coefficients, A is a matrix of the absorbances, and Eis a matrix of the residuals. As with CLS, the P matrix can bedetermined using known concentrations of a sample. In this scenario, theresidual error can be assumed to be zero because the ILS model can berecomputed until the residual error is sufficiently close to zero (e.g.,by setting a threshold value indicative of the error being sufficientlyclose to zero) and Equation 5 can be modified as:

P=C·A ⁻¹  EQN. 6

Using known concentrations for individual species yields values for theC matrix. The A matrix is constructed based on spectral measurementstaken from the sample with known concentrations (e.g., using a detectionsystem of FIG. 1, such as an FTIR spectrometer). The P matrix cantherefore be calculated using Equation 6 based on spectra measured fromknown concentrations of species in a sample.

The P matrix can then be used with Equation 5 to solve for unknownconcentrations of species in a sample. Specifically, a detection system(e.g., of FIG. 1), such as an FTIR system can be used obtain spectralmeasurements from a sample having unknown concentrations of individualspecies. The spectral measurements can be used to populate values forabsorbances in the A matrix. The P matrix, calculated using Equation 6based on known concentrations, can be used in Equation 5 to calculate aC matrix, thereby yielding the values for concentrations of individualspecies in the sample.

A system such as the system of FIG. 1 above can be used to detect,quantify and monitor siloxane compounds in a biogas incorporating any ofthe exemplary techniques as described above. The system can include, forexample, a source of a first beam of radiation (e.g., source 14 of FIG.1), an interferometer (e.g., interferometer 18 of FIG. 1), a sample cell(e.g., cell 22 of FIG. 1), a flow mechanism (e.g., flow system 82 ofFIG. 3), a cooled detector (e.g., detector 30 of FIG. 1), a processor(e.g., processor 34 of FIG. 1), and a housing (e.g., housing 42 ofFIG. 1) in which the source, the interferometer, the sample cell, thecooled detector and the processor are disposed. The interferometerreceives a first beam of radiation from the source and forms a secondbeam of radiation (e.g., the second beam reflected back and forth atotal of about 48 times in the sample cell, resulting in an effectivepathlength of about 10.18 meters) comprising an interference signal(e.g., interferometric signal). The sample cell is in opticalcommunication with the interferometer. The flow mechanism establishes aflow of a non-absorptive gas (e.g., a gas having substantially noinfrared absorptions in a specified wavelength range of interest) and asecond flow of a biogas through the sample cell (e.g., a pressured (e.g,3-5 psig) sample (e.g., 400 mL of biogas) introduced into the samplecell, the residence time of the biogas on the order of about 5 seconds).The detector (e.g., a cooled detector) is in optical communication withthe sample cell and receives a first interference signal propagatingthrough the non-absorptive gas in the sample cell and a secondinterference signal propagating through a sample gas in the sample cell,the sample gas comprising the biogas. The processor is in electricalcommunication with the detector (e.g., a cooled detector such as acryogenically (e.g., Stirling engine) cooled MCT(Mercury-Cadmium-Telluride) detector) and calculates a concentration ofat least one siloxane compound in the biogas. The processor calculatesthe concentration of at least one siloxane compound in the biogas basedon a first absorption spectrum and a second absorption spectrum usingchemometric techniques (e.g., such as the CLS and ILS techniques). Thefirst absorption spectrum is based on ratio of the first interferencesignal to the second interference signal from the detector. The secondabsorption spectrum is based on, at least, an individual absorptionspectrum for a known concentration of the at least one siloxanecompound.

In some embodiments, the sample cell (e.g., cell 22 of FIG. 1 with, forexample, an optical configuration described above for FIG. 2) includes aconcave reflective field surface (e.g., field surface 78 of FIG. 2) at afirst end of the sample cell and a substantially spherical, concavereflective objective surface (e.g., objective surface 74 of FIG. 2) at asecond end of the sample cell in a confronting relationship to the fieldsurface, the objective surface having a cylindrical component increasingcoincidence of foci in at least one plane to maximize throughput of thesecond beam of radiation propagating through the sample cell viamultiple reflections on each of the field surface and the objectivesurface.

In one embodiment, a computer readable product, tangibly embodied on aninformation carrier or a machine-readable storage device, is operable ona digital signal processor (e.g., processor 34 of FIG. 1) of a biogasdetection system (e.g., system of FIG. 1). The computer readable productincludes instructions operable to cause the digital signal processor toreceive a first spectral measurement (e.g., from a detector 30 ofFIG. 1) from a non-absorptive gas in a sampling cell (e.g., cell 22 ofFIG. 1), where the non-absorptive gas has substantially no infraredabsorptions in a specified wavelength range of interest. The computerproduct can also cause the digital signal processor to receive a secondspectral measurement from a sample gas comprising a biogas in thesampling cell and generate a first absorption spectrum (e.g., a measuredabsorption spectrum) based on a ratio of the first spectral measurementand the second spectral measurement. A second absorption spectrum (e.g.,a model absorption spectrum) can be generated/formulated based on, atleast, a first individual absorption spectrum for a known concentrationof at least one siloxane compound. The computer product can also causethe processor to calculate a concentration of one or more siloxanecompounds using the chemometric techniques described above (e.g.,performing a multiple regression analysis and mathematically fitting thesecond absorption spectrum to the first absorption spectrum to calculatea concentration of the at least one siloxane compound in the biogas).

As noted above, absorption spectra based on spectral measurements of abiogas in a sample cell and individual spectra for the individualcomponents/species in the biogas (e.g., individual spectra of thespecies in a biogas, such as, for example, siloxane compounds,hydrocarbon compounds, water or carbon dioxide) can be used to calculateconcentrations of individual species in a sample. Absorption spectra,such as the spectra shown in FIG. 11, can be used to generate a modelbased upon the calibrated absorption spectra (e.g., the secondabsorption spectrum as described above for FIG. 9) that arerepresentative of a compilation of the individual absorption spectra(e.g., based on concentration ranges and/or different spectral mixtures,depending on the method(s) of analysis used). Specifically, anabsorption spectrum based on spectral measurements of an unknown biogascan be used to generate an A matrix as described above, to calculateconcentrations of siloxanes using, for example, Equations 7 and 11.Individual spectra, obtained based on measurements taken from knownconcentrations of species, can be used to generate a model spectrumrepresentative of the individual species. Individual spectra for knownconcentrations can be used to calculate the P Matrix or K Matrix asdescribed above in Equations 8 and 12. The model can include, forexample, the P matrix or the K matrix (e.g., determined by using knownconcentrations of species) which can be used to calculate concentrationsof siloxanes, using, for example, Equations 7 and 11. FIG. 11 showsspectra of data that can be used to quantify concentrations of siloxanesin a biogas, according to an illustrative embodiment of the invention.Each absorbing species in a sample has a unique absorption vs. frequencydistribution (i.e. absorption spectrum). Using chemometric algorithms(e.g., multiple regression analysis), each component can becharacterized and quantified, such that individual species of siloxanecompounds can be detected, even in the presence of other interferingabsorbers (e.g., hydrocarbon compounds such as methane or ethane).

The above-described systems and methods can be implemented in digitalelectronic circuitry, in computer hardware, firmware, and/or software.The implementation can be as a computer program product (i.e., acomputer program tangibly embodied in an information carrier). Theimplementation can, for example, be in a machine-readable storage deviceand/or in a propagated signal, for execution by, or to control theoperation of, data processing apparatus. The implementation can, forexample, be a programmable processor, a computer, and/or multiplecomputers.

A computer program can be written in any form of programming language,including compiled and/or interpreted languages, and the computerprogram can be deployed in any form, including as a stand-alone programor as a subroutine, element, and/or other unit suitable for use in acomputing environment. A computer program can be deployed to be executedon one computer or on multiple computers at one site.

Method steps can be performed by one or more programmable processorsexecuting a computer program to perform functions of the invention byoperating on input data and generating output. Method steps can also beperformed by and an apparatus can be implemented as special purposelogic circuitry. The circuitry can, for example, be a FPGA (fieldprogrammable gate array) and/or an ASIC (application-specific integratedcircuit). Modules, subroutines, and software agents can refer toportions of the computer program, the processor, the special circuitry,software, and/or hardware that implement that functionality.

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor receives instructions and data from a read-only memory or arandom access memory or both. The essential elements of a computer are aprocessor for executing instructions and one or more memory devices forstoring instructions and data. Generally, a computer can include, can beoperatively coupled to receive data from and/or transfer data to one ormore mass storage devices for storing data (e.g., magnetic,magneto-optical disks, or optical disks).

To provide for interaction with a user, the above described techniquescan be implemented on a computer having a display device. The displaydevice can, for example, be a cathode ray tube (CRT) and/or a liquidcrystal display (LCD) monitor. The interaction with a user can, forexample, be a display of information to the user and a keyboard and apointing device (e.g., a mouse or a trackball) by which the user canprovide input to the computer (e.g., interact with a user interfaceelement). Other kinds of devices can be used to provide for interactionwith a user. Other devices can, for example, be feedback provided to theuser in any form of sensory feedback (e.g., visual feedback, auditoryfeedback, or tactile feedback). Input from the user can, for example, bereceived in any form, including acoustic, speech, and/or tactile input.

The above described techniques can be implemented in a distributedcomputing system that includes a back-end component. The back-endcomponent can, for example, be a data server, a middleware component,and/or an application server. The above described techniques can beimplemented in a distributing computing system that includes a front-endcomponent. The front-end component can, for example, be a clientcomputer having a graphical user interface, a Web browser through whicha user can interact with an example implementation, and/or othergraphical user interfaces for a transmitting device. The components ofthe system can be interconnected by any form or medium of digital datacommunication (e.g., a communication network). Examples of communicationnetworks include a local area network (LAN), a wide area network (WAN),the Internet, wired networks, and/or wireless networks.

The system can include clients and servers. A client and a server aregenerally remote from each other and typically interact through acommunication network. The relationship of client and server arises byvirtue of computer programs running on the respective computers andhaving a client-server relationship to each other.

While this invention has been particularly shown and described withreferences to preferred embodiments thereof, it will be understood bythose skilled in the art that various changes in form and details may bemade therein without departing from the scope of the inventionencompassed by the appended claims.

1. A method for monitoring of siloxane compounds in a biogas, the methodcomprising: generating a first absorption spectrum based on a ratio of afirst spectral measurement from a non-absorptive gas havingsubstantially no infrared absorptions in a specified wavelength range ofinterest and a second spectral measurement from a sample gas comprisingthe biogas; and calculating a concentration of at least one siloxanecompound in the biogas using a second absorption spectrum based on, atleast, a first individual absorption spectrum for a known concentrationof the at least one siloxane compound.
 2. The method of claim 1, whereincalculating comprises performing, using a processor, multiple regressionanalysis using the first absorption spectrum and the second absorptionspectrum.
 3. The method of claim 2, further comprising performing themultiple regression analysis using Classical Least Squares (CLS),Partial Least Squares (PLS), Inverse Least Squares (ILS) or PrincipalComponent Analysis (PCA).
 4. The method of claim 1, further comprisingcreating the second absorption spectrum based on, at least, the firstindividual absorption spectrum and individual absorption spectra for oneor more additional siloxane compounds, hydrocarbon compounds, water orcarbon dioxide.
 5. The method of claim 4, wherein the second absorptionspectrum is a model based on known concentrations of the siloxanecompounds, hydrocarbon compounds, water or carbon dioxide.
 6. The methodof claim 1, wherein the second absorption spectrum is a model based on,at least, the first individual absorption spectrum and whereincalculating comprises: providing at least one variable representing theconcentration of the at least one siloxane compound; and determining avalue for the at least one variable such that that the second absorptionspectrum is substantially similar to the first absorption spectrum. 7.The method of claim 1, further comprising calculating, using aprocessor, the concentration of at least one siloxane compound inreal-time and in-situ.
 8. The method of claim 1, further comprisingtaking the second spectral measurement over an acquisition period ofabout 10 seconds to about 20 seconds.
 9. The method of claim 1, whereinthe at least one siloxane compound is selected from a group consistingof L2-siloxane, L3-siloxane, L4-siloxane, L5-siloxane, D3-siloxane,D4-siloxane, D5-siloxane, or D6-Siloxane.
 10. The method of claim 1,further comprising providing the non-absorptive gas and providing thebiogas to a sample cell, the sample cell comprising: a concavereflective field surface at a first end of the sample cell; and asubstantially spherical, concave reflective objective surface at asecond end of the sample cell in a confronting relationship to the fieldsurface, the objective surface having a cylindrical component increasingcoincidence of foci in at least one plane to maximize throughput of thesecond beam of radiation propagating through the sample cell viamultiple reflections on each of the field surface and the objectivesurface.
 11. A method for monitoring a level of at least one siloxanecompound in a biogas, the method comprising: providing a non-absorptivegas to a sample cell, the non-absorptive gas having substantially noinfrared absorptions in a specified wavelength range of interest; takinga first spectral measurement from the sample cell; providing a biogas tothe sample cell, the biogas comprising at least one siloxane compound;taking a second spectral measurement from the sample cell; generating afirst absorption spectrum based on a ratio of the first spectralmeasurement to the second spectral measurement; and calculating aconcentration of the at least one siloxane compound in the biogas byusing the first absorption spectrum and a second absorption spectrum,wherein the second absorption spectrum is based on, at least, anindividual absorption spectrum for a known concentration of the at leastone siloxane compound.
 12. The method of claim 11, further comprisingperforming, using a processor, a multiple regression analysis tocalculate the concentration of at least one siloxane compound.
 13. Themethod of claim 11, further comprising performing the multipleregression analysis using Classical Least Squares (CLS), Partial LeastSquares (PLS), Inverse Least Squares (ILS), or Principal ComponentAnalysis (PCA).
 14. The method of claim 11, wherein the secondabsorption spectrum is a model based on, at least, the individualabsorption spectrum for the at least one siloxane compound andindividual absorption spectra for one or more additional siloxanecompounds, hydrocarbon compounds, water or carbon dioxide.
 15. Themethod of claim 14, wherein the second absorption spectrum is a modelbased on known concentrations of the siloxane compounds, hydrocarboncompounds, water or carbon dioxide.
 16. The method of claim 14, furthercomprising determining a value for the concentration for the at leastone siloxane compound such that the second absorption spectrum issubstantially similar to the first absorption spectrum.
 17. The methodof claim 11, further comprising calculating the concentration of the atleast one siloxane compound real-time and in-situ.
 18. The method ofclaim 11, further comprising providing the biogas from animal waste,wastewater or a landfill.
 19. The method of claim 11, further comprisingshutting off a turbine generator when the concentration of at least onesiloxane compound reaches a threshold value.
 20. The method of claim 11,further comprising taking the second spectral measurement from thesample cell in a wavelength range of about 8 microns to about 12microns.
 21. The method of claim 11, further comprising taking thesecond spectral measurement over a 10 second acquisition time period.22. The method of claim 11, wherein taking the second spectralmeasurement comprises acquiring an infrared signal from the sample cell.23. The method of claim 11, further comprising generating the secondabsorption spectrum based on, at least, an individual absorption spectrafor L2-siloxane, L3-siloxane, L4-siloxane, L5-Siloxane, D3-siloxane,D4-siloxane, D5-Siloxane, D6-siloxane, Methane, Ethane, water, carbondioxide, or an combination thereof.
 24. A system for monitoring at leastone siloxane compound in a biogas, the system comprising: a source of afirst beam of radiation; an interferometer receiving the first beam ofradiation from the source and forming a second beam of radiationcomprising an interference signal; a sample cell in opticalcommunication with the interferometer; a flow mechanism establishing afirst flow of a non-absorptive gas having substantially no infraredabsorptions in a specified wavelength range of interest and a secondflow of a biogas through the sample cell; a cooled detector in opticalcommunication with the sample cell, the cooled detector receiving: afirst interference signal propagating through the non-absorptive gas inthe sample cell; and a second interference signal propagating through asample gas in the sample cell, the sample gas comprising the biogas; aprocessor in electrical communication with the cooled detector, theprocessor configured to calculate a concentration of at least onesiloxane compound in the biogas based on: a first absorption spectrumbased on ratio of the first interference signal to the secondinterference signal; and a second absorption spectrum based on, atleast, an individual absorption spectrum for a known concentration ofthe at least one siloxane compound; and a housing in which the source,the interferometer, the sample cell, the cooled detector and theprocessor are disposed.
 25. The system of claim 24, wherein the samplecell comprises: a concave reflective field surface at a first end of thesample cell; and a substantially spherical, concave reflective objectivesurface at a second end of the sample cell in a confronting relationshipto the field surface, the objective surface having a cylindricalcomponent increasing coincidence of foci in at least one plane tomaximize throughput of the second beam of radiation propagating throughthe sample cell via multiple reflections on each of the field surfaceand the objective surface.
 26. The system of claim 24, wherein thesecond absorption spectrum is a model based on, at least, the individualabsorption spectrum for the at least one siloxane compound andindividual absorption spectra for one or more additional siloxanecompounds, hydrocarbon compounds, water or carbon dioxide.
 27. Thesystem of claim 26, wherein the second absorption spectrum is a modelbased on, at least, known concentrations of the siloxane compounds,hydrocarbon compounds, water or carbon dioxide.
 28. A computer readableproduct, tangibly embodied on an information carrier or amachine-readable storage device, and operable on a digital signalprocessor for a biogas detection system, the computer readable productincluding instructions operable to cause the digital signal processorto: receive a first spectral measurement from a non-absorptive gas in asampling cell, the non-absorptive gas having substantially no infraredabsorptions in a specified wavelength range of interest; receive asecond spectral measurement from a sample gas comprising a biogas in thesampling cell; generate a first absorption spectrum based on a ratio ofthe first spectral measurement and the second spectral measurement;generate a second absorption spectrum based on, at least, a firstindividual absorption spectrum for a known concentration of at least onesiloxane compound; and perform a multiple regression analysis using thefirst absorption spectrum and the second absorption spectrum tocalculate a concentration of the at least one siloxane compound in thebiogas.
 29. The product of claim 28, wherein the digital processor isconfigured to determine a value for the concentration of the at leastone siloxane compound in the biogas such that the second absorptionspectrum is substantially similar to the first absorption spectrum. 30.The product of claim 28, wherein the digital processor is configured togenerate the second absorption spectrum which is a model based on, atleast, the first individual absorption spectrum and an individualabsorption spectra for one or more additional siloxane compounds,Hydrocarbon compounds, water or carbon dioxide.